ULMFiT Sentiment

Challenge Problem Frequently Asked Questions

What are you looking for in challenge submissions?

1. Problem solving ability - did you understand the problem correctly, and did you take logical steps to solve it?

2. Machine learning skills - what sort of models did you use? How rigorous was your exploratory analysis of the data, your choice and fine tuning of models, and your assessment of results.

3. Coding skills- does your python look presentable or do you code like a scientist?

4. Communication skills - is your solution readable and well explained? Messiness and raw code with no explanation does not reflect well on your potential for working well with our business partners during the fellowship.

What are some common mistakes I should avoid?

Skipping exploratory analysis and feature engineeringDo not jump straight into fitting models without demonstrating to us, in your Jupyter notebook, that you have understood and thought about the dataset.

Choosing models with no explanationPleaseuse the notebook to explain your thought process. We care about this as much as we care about your results.

Unreadable notebooksMake sure to run your notebook before sharing so that we can see the results. We won't be running your code on our machines. On the other hand, please do not print out the entire dataset or endless rounds of epochs.

Overly simplistic final resultsYour final results should consist of more than a single number or percentage printout. Explain why you chose the success metrics you chose, and analyze what your output means.

When are the challenges due?

What is the next step after submitting my challenge problem?

After we review your challenge, candidates selected to move forward in the application process will receive an email with an invitation to schedule a 45-minute interview with a mentor or former fellow. Be prepared to discuss your challenge. You will likely be asked to explain why you chose the model(s) you used, and asked questions gauging how deeply you understand the model(s).